Method for operating measuring device, computing unit and computer program product

Abstract

The invention relates to a computer-implemented method for operating a measuring device, in particular a wall diagnostic device, comprising: receiving radar data from a radar sensor unit of the measuring device, the radar data mapping a wall to be diagnosed; wall diagnosis is carried out by carrying out an analysis on the radar data by means of a diagnostic module of the measuring device and diagnosis results are provided, the wall diagnosis comprising: carrying out, by means of the diagnostic module, object recognition on an object arranged in the wall, the object recognition comprising object detection and object classification; and providing the diagnosis result of the diagnosis module to a display unit of the measurement device so as to display the diagnosis result to a user of the measurement device, the diagnosis result including at least the object position in the wall and the object type of the object. The invention also relates to a computing unit and a computer program product.

Description

Technical Field

[0001] The invention relates to a method for operating a measuring device, in particular a wall diagnostic device. Background Art

[0002] Diagnostic devices for diagnosing walls and for detecting objects formed in walls are known from the prior art. Summary of the Invention

[0003] One object of the present invention is to provide an improved method for operating a measuring device, in particular a wall diagnostic device.

[0004] This object is achieved by a computer-implemented method for operating a measuring device, in particular a wall diagnostic device. Advantageous embodiments are described below.

[0005] According to one aspect, a computer-implemented method for operating a measuring device, in particular a wall diagnostic device, is provided, the method comprising:

[0006] receiving radar data from a radar sensor unit of a measuring device, wherein the radar data maps a wall to be diagnosed;

[0007] The diagnostic module of the measuring device analyzes the radar data to perform wall diagnosis and provides a diagnostic result, wherein the wall diagnosis includes:

[0008] Performing object recognition on objects arranged in the wall by the diagnostic module, wherein the object recognition includes object detection and object classification; and

[0009] The diagnosis result of the diagnosis module is provided to a display unit of the measuring device so as to display the diagnosis result to a user of the measuring device, wherein the diagnosis result at least includes a position of the object in the wall and an object type of the object.

[0010] This achieves the technical advantage of providing an improved method for operating a measuring device, in particular a wall diagnostic device. To this end, a diagnostic module performs wall diagnostics based on radar data from at least one radar sensor unit of the measuring device that maps to the wall to be diagnosed. To this end, the wall diagnostics include object recognition by the diagnostic module of at least one object disposed in the wall.

[0011] Object recognition includes object detection and object classification. Object detection involves determining the object's position within a wall, while object classification involves determining the object type of an object located within the wall. Wall diagnosis results, including at least the object's position and type, are also displayed on the measuring device's display unit. With a suitably configured diagnostic module, wall diagnosis, including object recognition, can be performed solely based on radar data from the radar sensor unit.

[0012] Because the diagnostic module is configured to perform corresponding object identification, including object detection and classification, based solely on radar data, the need to consider additional sensor information from additional sensors for object identification can be eliminated. This eliminates the need for additional sensors beyond the radar sensor unit, allowing for a more compact measuring device.

[0013] According to one embodiment, the wall diagnosis further includes:

[0014] The diagnosis module performs a wall type classification and determines the wall type of the wall.

[0015] This achieves the technical advantage that, in addition to object recognition, wall type classification can also be performed based solely on the radar data from the radar sensor unit. During the wall type classification, a correspondingly configured diagnostic module can determine or classify the different wall types of the wall to be inspected. Automatic wall type classification eliminates the need for the user to enter the corresponding wall type of the wall to be inspected as additional information for wall diagnosis, as is known in the prior art. Instead, the diagnostic module automatically determines the wall type based on the radar data from the radar sensor unit.

[0016] The wall type can be used as additional information, for example for background correction, in wall diagnosis and object recognition. Alternatively or additionally, the detected wall type can be displayed to the user as additional information in a display unit as a diagnosis result.

[0017] According to one embodiment, the wall diagnosis further includes:

[0018] An object depth determination is carried out by the diagnostic module and the object depth of the object in the wall is determined, wherein the object depth is defined as the distance of the object from the surface of the wall.

[0019] This provides the technical advantage that, in addition to object recognition, the measuring device can also determine the object's depth within a wall based on the radar data from the radar sensor unit. The object depth describes the distance of the object from the wall surface. A suitably configured diagnostic module can determine the object depth based solely on the radar data from the radar sensor unit.

[0020] According to one embodiment, the analysis of the radar data further includes:

[0021] An object extension determination is carried out by the diagnosis module and the object extension of the object along a predefined direction is determined.

[0022] This achieves the technical advantage that the object extent of objects detected in a wall can also be determined based on the radar data from the radar sensor unit. The object extent describes the extent of an object relative to at least one spatial direction, preferably two spatial directions, and particularly preferably three spatial directions. This enables one-dimensional, preferably two-dimensional, and particularly preferably three-dimensional information about the extent of an object in a wall.

[0023] According to one embodiment, the wall diagnosis further includes:

[0024] Preprocessing the radar data of the radar sensor unit by a first preprocessing module and providing input data to the wall type classification module of the diagnosis module;

[0025] Implementing wall type classification based on input data provided by the first pre-processing module and providing wall type information by the wall type classification module of the diagnosis module;

[0026] Preprocessing the radar data by a second preprocessing module of the diagnosis module while taking the wall type information into consideration and providing input data for an object recognition module of the diagnosis module;

[0027] The object recognition module performs object recognition based on the input data provided by the second pre-processing module and provides a diagnosis result.

[0028] This makes it possible to achieve the technical advantage that a precise wall diagnosis can be achieved based on the radar data of the radar sensor unit by means of a correspondingly designed diagnostic module.

[0029] By preprocessing the radar data from the radar sensor unit, the radar data can be converted into the form required for wall diagnosis. Through a correspondingly constructed wall type classification module, the preprocessed radar data can be classified into corresponding wall types and the corresponding wall type of the wall can be determined.

[0030] The object recognition module can be used to identify objects located in a wall based on preprocessed radar data and taking into account the wall type provided by the wall type classification module. The object recognition module is configured to detect and classify objects located in the wall based on preprocessed radar data and taking into account the provided wall type. The proposed diagnostic module architecture allows for the most accurate and reliable wall diagnosis possible for the wall to be inspected.

[0031] According to one embodiment, radar data is received from a plurality of positions of a measurement device relative to a wall, wherein the method further comprises:

[0032] Motion data from a motion detection unit of the measurement device is received, wherein the motion data maps motion of the measurement device relative to the wall between a plurality of positions of the measurement device relative to the wall.

[0033] This achieves the technical advantage that, by taking into account the motion data from the motion detection unit, a wall diagnosis can be performed for different positions of the measuring device relative to the wall. The motion data maps the movement of the measuring device between multiple positions of the measuring device relative to the wall. Therefore, taking into account the motion data enables accurate positioning or position determination of the measuring device relative to the wall. Based on this, a position-dependent wall diagnosis can be performed for different positions of the measuring device relative to the wall.

[0034] Furthermore, the movement of the measuring device between different positions relative to the wall can be realized based on the movement data. This enables wall diagnosis during the movement of the measuring device relative to the wall, making it possible to examine a larger spatial area of ​​the wall than would be possible with the range of the radar sensor unit. This speeds up the wall diagnosis and enables a consistent and accurate analysis of the wall to be examined. More precise inspection of spatial areas.

[0035] According to one embodiment, the method further comprises:

[0036] checking whether it is detected that the measuring device has moved a predefined distance relative to the wall based on the motion data of the motion detection unit;

[0037] If no motion is detected, storing the radar data and re-performing the check;

[0038] If movement of the predefined distance is detected, the wall diagnosis is performed based on the radar data by the diagnosis module.

[0039] This achieves the technical advantage of performing wall diagnostics at predetermined intervals while the measuring device is in motion relative to the wall. Wall diagnostics are performed based on radar data recorded by the measuring device during its motion relative to the wall over a predefined distance. Therefore, during wall diagnostics, the measuring device can scan the wall to be inspected at predefined distances. This enables accurate wall diagnostics of extended spatial areas of the wall to be inspected.

[0040] According to one embodiment, the wall diagnosis further includes:

[0041] If there is a predefined distance correlation between the detected and classified partial objects based on the radar data received at different positions of the measuring device relative to the wall, the partial objects are combined to form a coherent object.

[0042] This provides the technical advantage of combining detected and classified partial objects based on radar data from different positions of the measuring device relative to the wall into a coherent object, thereby enabling precise wall diagnosis including precise object identification. Wall diagnosis is performed according to the above-described embodiment while the measuring device is in motion relative to the wall.

[0043] For motion segments defined by predefined distances, the diagnostic module determines partial results during wall diagnosis—i.e., partial objects of the object to be identified within the wall. The classified partial objects describe a portion of an extended object that is mapped into the partial segments of motion by the movement of the measuring device relative to the wall. By combining the correspondingly classified partial objects into a single overall object, the extended object can be described and identified in detail. This enables precise wall diagnosis and identification of extended objects.

[0044] According to one embodiment, the measuring device further comprises at least one inductive sensor and / or eddy current sensor and / or capacitive sensor and / or AC sensor and / or nuclear magnetic resonance (NMR) sensor and / or ultrasonic sensor for providing additional sensor data, wherein the diagnostic module is configured to perform the wall diagnosis taking into account the additional sensor data.

[0045] This provides the technical advantage that, by providing additional sensor data from additional sensors that each detect a different physical measurement variable, additional information, in addition to the information from the radar data of the radar sensor unit, can be incorporated into the wall diagnosis. This additional information, which is preferably complementary to the information from the radar data of the radar sensor unit, enables further refinement of the wall diagnosis or object identification.

[0046] According to one specific embodiment, the diagnostic module comprises at least one correspondingly trained artificial intelligence, which is configured to perform object recognition and / or wall classification and / or object depth determination based on the radar data and / or the additional sensor data.

[0047] This achieves the technical advantage that by configuring the diagnostic module as a correspondingly trained artificial intelligence, which is trained to perform object recognition and / or wall classification and / or object depth determination and / or object extension determination based on radar data or, if necessary, information from additional sensors, a reliable and high-performance diagnostic module can be provided. The use of artificial intelligence technology allows for precise wall diagnosis.

[0048] According to one aspect, a computing unit is provided, which is configured to carry out a method for operating a measuring device according to any of the above embodiments.

[0049] According to one aspect, a computer program product is provided, which comprises instructions which, when the program is executed by a data processing unit, cause the data processing unit to carry out a method for operating a measuring device according to one embodiment. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Embodiments of the present invention will be described with reference to the following drawings. These drawings show:

[0051] Figure 1 A schematic illustration of a measuring device according to one embodiment;

[0052] Figure 2 Another schematic illustration of a measuring device according to another embodiment;

[0053] Figure 3 Another schematic illustration of a measuring device according to another embodiment;

[0054] Figure 4 Schematic illustration of a measurement of a measuring device according to one embodiment,

[0055] Figure 5 Another schematic illustration of a measuring device according to another embodiment;

[0056] Figure 6 A flow chart of a method for operating a measuring device according to one embodiment,

[0057] Figure 7 Another flow chart of a method for operating a measuring device according to another embodiment,

[0058] Figure 8Another flow chart of a method for operating a measuring device according to another embodiment,

[0059] Figure 9 Schematic illustration of a computer program product. DETAILED DESCRIPTION

[0060] Figure 1 A schematic illustration of a measuring device 100 according to one specific embodiment is shown.

[0061] The present invention relates to a measuring device, and in particular to a wall diagnostic device for inspecting a wall 105 to be processed. Wall diagnostic devices for detecting objects arranged in a wall are known in the prior art. Such devices allow a user to inspect the wall to be processed based on the objects arranged in the wall, so that planned work, such as drilling a hole in the wall, can be carried out based on this information, in a manner that avoids damage to the objects arranged in the wall.

[0062] In the embodiment shown, the measuring device 100 includes a housing 150 having a handle 152 configured for a user to hold the measuring device 100 , a display unit 111 configured to display a diagnosis result 109 of a wall diagnosis, and operating elements 154 configured to switch the measuring device 100 into different operating modes.

[0063] According to the present invention, measuring device 100 comprises at least one radar sensor unit 101. Radar sensor unit 101 can transmit radar signals in the direction of a wall 105 to be inspected and can receive radar signals reflected by wall 105.

[0064] For example, radar sensor unit 101 may be configured as a narrowband radar detector device in the frequency range of 2.4 GHz to 2.4835 GHz or as an ultra-wideband radar detector device in the frequency range of 1.8 GHz to 5.8 GHz.

[0065] Measuring device 100 also includes a diagnostic module 107, which can be executed on computing unit 151 of measuring device 100, for performing wall diagnostics. Diagnostic module 107 is configured to perform corresponding diagnostics of the wall to be inspected based on radar data 103 from radar sensor unit 101. Radar data 103 from radar sensor unit 101 represents wall 105 to be inspected and objects 113 that may be located within wall 105.

[0066] The wall diagnosis performed by diagnostic module 107 includes at least the implementation of object recognition. Object recognition includes object detection and object classification of objects 113 located in wall 105. Object detection includes at least the determination of object position 115. The object position describes the position of the object located in wall 105 relative to the coordinate system defined by measuring device 100. Object classification of detected objects 113 includes at least the determination of object type 117 of detected objects 113.

[0067] The diagnostic results of the wall diagnosis determined in this manner, i.e., at least the determined object position 115 and / or the determined object type 117 of the object 113 arranged in the wall 105, are then displayed to the user of the measuring device 100 on the display unit 111 of the measuring device 100. The display unit 111 can be configured as a corresponding display screen, for example, and the diagnostic results 109 can be displayed visually. In addition, the display of the diagnostic results 109 can be assisted by acoustic and / or tactile signals. For example, the tactile signals can be implemented by corresponding vibration signals.

[0068] In this case, object 113 can be displayed, for example, by a corresponding symbol in a display screen. Object 113 can be displayed in a corresponding object position 115 in the display screen. Object extent 121 can be visualized by the corresponding size of the displayed symbol. The corresponding object type 117 of object 113 can be visualized by a corresponding term, a colored background of the symbol, or a specific shape of the symbol representing object 113.

[0069] Alternatively, the wall diagnosis can additionally include determining a wall type 123 in the form of a wall type classification of the wall 105 to be inspected. Wall type 123 describes the respective type of wall 105 to be inspected. For example, the wall type can be assigned to a respective wall type category, which can include: concrete wall, lightweight / drywall wall, brick wall and / or wall block, floor heating, wall heating, or wall types found in similar buildings.

[0070] According to one embodiment, diagnostic module 107 is further configured to determine object depth 119 of object 113 within wall 105 based on radar data 103. Object depth 119 is defined by the distance of an object formed in wall 105 from the surface of wall 105. This distance can be defined, for example, relative to the object surface or relative to the object's center point. The distance to the surface of wall 105 describes the shortest distance defined by a direction perpendicular to the surface of wall 105.

[0071] According to one embodiment, diagnostic module 107 is further configured to determine object extent 121 of object 113 in at least one predefined direction based on radar data 103. Object extent 121 of object 113 describes the spatial extent of object 113 in at least one spatial direction, preferably in two spatial directions, and particularly preferably in three spatial directions. Thus, object 113 can be described as a one-dimensional, two-dimensional, or three-dimensional object 113.

[0072] In typical use, measuring device 100 is placed on the wall surface of a wall 105 to be inspected. Radar signals are transmitted toward wall 105 via radar sensor unit 101, and radar signals reflected by wall 105 or objects 113 located behind it are received. Based on radar data 103 from radar sensor unit 101, diagnostic module 107 performs the aforementioned wall diagnosis and determines corresponding diagnostic results 109.

[0073] Diagnosis result 109 may include, for example, object position 115 and / or object type 117 of object 113 arranged in wall 105. Alternatively or additionally, diagnosis result 109 may include wall type 123 of wall 105 and / or object depth 119 and / or object extent 121 of object 113.

[0074] The diagnosis result 109 formed in this way can then be displayed to the user of the measuring device 100 on the display unit 111 of the measuring device 100. The display unit 111 can be designed as a corresponding display screen, for example. The diagnosis result 109 can be displayed in a graphical form or in a textual form on the display unit 111.

[0075] According to one embodiment, measuring device 100 further includes a motion detection unit 141. Motion detection unit 141 can be used to detect the movement of measuring device 100 relative to wall 105. To this end, motion detection unit 141 can include, for example, at least one rolling element. When the rolling element is supported on the wall surface of wall 105, the movement of measuring device 100 relative to wall 105 can be detected when measuring device 100 moves along motion direction 153 due to the rolling of the rolling element. Alternatively, motion detection unit 141 can have other configurations that can detect the relative movement of measuring device 100 relative to wall 105.

[0076] By moving measuring device 100 relative to wall 105, radar data 103 of radar sensor unit 101 can be recorded for a plurality of different positionings of measuring device 100 relative to wall 105. This allows for an inspection of wall 105 over a larger spatial area than that given by the range of action of radar sensor unit 101. This allows for the detection of objects 113 having a larger spatial extent than the range of action of radar sensor unit 101.

[0077] While measuring device 100 is moving along movement device 153, radar data 103 from radar sensor unit 101 can be continuously recorded. Based on these radar data 103, wall diagnostics can be evaluated by diagnostic module 107 while measuring device 100 is moving along movement direction 153. This allows for accelerated wall diagnostics that take into account the positioning of measuring device 100 relative to wall 105.

[0078] Depending on its embodiment, diagnostic module 107 is designed as a correspondingly trained artificial intelligence 125. Artificial intelligence 125 is trained to perform the aforementioned wall diagnosis based on radar data 103 from radar sensor unit 101 and to determine at least object position 115 and object type 117 of object 113 located in wall 105. Object classification or determination of object type 117 includes assigning detected object 113 to a predefined object class.

[0079] Object categories here can include: metallic / non-metallic objects, cables for low voltage, cables with single-phase AC signals, cables with multi-phase AC signals, wooden beams, metal beams, plastic pipes, water-filled plastic pipes, such as water supply pipes, non-water-filled plastic pipes, such as drainage pipes, or other common elements built into building walls.

[0080] Furthermore, artificial intelligence 125 may be trained to determine wall type 123 of wall 105 to be inspected based at least on radar data 103 of radar sensor unit 101. Possible wall types 123 may include: concrete walls, lightweight / drywall walls, brickwork and / or brickwork walls, floor heating, wall heating, or other common wall types constructed in buildings.

[0081] According to one embodiment, in addition to radar sensor unit 101, measuring device 100 may also include other additional sensors, by means of which additional physical variables can be detected. For example, measuring device 100 may include an inductive sensor, an eddy current sensor, a capacitive sensor, an AC sensor, a nuclear magnetic resonance sensor, an ultrasonic sensor, or other sensors commonly used in wall diagnostic equipment.

[0082] Diagnostic module 107, in particular a correspondingly trained artificial intelligence 125, can be configured to perform the aforementioned wall diagnosis based on radar data 103 from radar sensor unit 101 and taking into account additional sensor information from other sensors. The additional information from these additional sensors can be used, in particular, to identify objects 113 located in wall 105. The additional sensor information can optionally enable improved detection of objects 113 and, if necessary, improved classification of objects 113.

[0083] In particular, the material of the object 113, such as a metallic material or a non-metallic material, may be refined and classified, for example, by using additional sensor information.

[0084] Figure 2 A further schematic illustration of a measuring device 100 according to another specific embodiment is shown.

[0085] In the illustrated embodiment, in addition to diagnostic module 107, measuring device 100 also includes a preprocessing module 127. To perform wall diagnosis, measuring device 100 first receives radar data 103 from radar sensor unit 101. Received radar data 103 is preprocessed by preprocessing module 127. Preprocessing by preprocessing module 127 can, for example, convert the radar data into a corresponding data structure required for wall diagnosis by diagnostic module 107.

[0086] As described above, the diagnostic module 107 generates the aforementioned diagnostic result 109 during the wall diagnostic process. Diagnostic result 109 may include, for example, an object position 115 of an object 113 located within the wall 105 to be inspected, an object type 117, an object depth 119, an object extension 121, and / or a wall type 123 of the wall 105 to be inspected. The correspondingly generated diagnostic result 109 may then be displayed on the display unit 111 of the measuring device 100.

[0087] According to one embodiment, in addition to radar data 103 from radar sensor unit 101, additional sensor information from the aforementioned additional sensors may also be considered in the wall diagnosis of diagnosis module 107. Preprocessing module 127 may accordingly perform corresponding preprocessing of the additional sensor information.

[0088] In the illustrated embodiment, diagnosis module 107 includes a wall type classification module 129 and an object recognition module 131. Preprocessing module 127 includes a first preprocessing module 135 and a second preprocessing module 137. First preprocessing module 135 includes S-matrix reduction 155. Second preprocessing module 137 includes background correction 157, inverse fast Fourier transform 159, and focusing and migration 161. When radar data 103 is preprocessed by preprocessing module 127, radar data 103 is first preprocessed by first preprocessing module 135 and S-matrix reduction 155 included therein.

[0089] Here, first preprocessing module 135 generates input data 133 based on radar data 103. Input data 133 serves as input data for wall type classification module 129. Wall type classification module 129 classifies the wall 105 to be inspected into a certain type based on input data 133 and generates wall type information 139. Wall type information 139 contains the wall type 123 of the wall 105 to be inspected determined in the wall type classification.

[0090] Second preprocessing module 137 then performs preprocessing based on radar data 103 and wall type information 139. Background correction 157 is performed on radar data 103, taking into account wall type 123 ascertained in wall type information 139. Depending on wall type 123 of wall 105 to be inspected, different effects on radar data 103 may occur.

[0091] These effects, which are primarily due to the respective wall type 123 and may affect object recognition, can be corrected by background correction 157. After background correction, further preprocessing can be performed by performing an inverse fast Fourier transform 159 or focusing and shifting 161, and new input data 133 can be generated for the object recognition module 131. Based on the input data 133 provided by the second preprocessing module 137, the object recognition module 133 recognizes an object 113 located in the wall 105 to be inspected and determines at least the object position 115 and the object type 117 of the respective object 113. Additionally, the object recognition module 131 can determine the object depth 119 and the object extent 121.

[0092] According to one specific embodiment, the diagnostic module is further configured to determine an object depth of an object within the wall based on the radar data, wherein the object depth is defined by the distance of the object formed in the wall from the surface of the wall.

[0093] Preprocessing is optional. Depending on the algorithm used for diagnostic module 107, completely unprocessed radar echoes of different frequencies can be used as radar data 103 and as input data for diagnostic module 107. Alternatively, radar data 103 processed in multiple steps can be used. Preprocessing steps include, for example, converting the signal from the frequency domain to the time domain or range space, background removal, denoising, and signal normalization. In the case of radar data 103 in complex form, only the absolute values ​​can be processed. Alternatively or additionally, phase information can be taken into account.

[0094] Figure 3 A further schematic illustration of a measuring device 100 according to another specific embodiment is shown.

[0095] In the embodiment shown, the diagnostic module 107 includes a plurality of processing paths 102 extending in parallel. In each processing path 102 there is a pre-processing module 127, a diagnostic module 107 (eg, including a Figure 2 The wall type classification module 129 and / or object recognition module 131) and the post-processing module 163 of the embodiment.

[0096] exist Figure 3 In the example, radar data 103 is primarily presented as input data for wall diagnosis. However, in addition to the radar data shown, additional information from additional sensors can also be used as input data for wall diagnosis. Different information from different sensor types can be processed in different parallel processing paths 102, and corresponding wall diagnosis can be performed separately based on the different sensor information. After the wall diagnosis is completed, the summary module can aggregate the analysis results of each component into a wall diagnosis result 109.

[0097] Alternatively or additionally, different partial aspects of the wall diagnosis can also be performed by different processing paths 102 based on the same sensor information.

[0098] For example, the various processing paths 102 can process different radar data 103 that were recorded for different positions of measuring device 100 relative to wall 105 during the movement of measuring device 100 relative to wall 105. Radar data 103 that thus map different areas of wall 105 and were recorded temporally sequentially during the movement of measuring device 100 relative to wall 105 can then be processed in the various processing paths 102 by the modules shown.

[0099] The different processing paths here carry out independent wall diagnoses, which include at least determining an object position 115 and / or an object type 117 of an object 113 arranged in the wall 105 .

[0100] The partial results of the independent wall diagnosis of different regions of wall 105, provided in the various processing paths 102, can be combined into a coherent diagnosis result 109 via aggregation module 165. The coherent diagnosis result describes the wall diagnosis for the coherent spatial region scanned during the movement of measuring device 100 relative to wall 105 and mapped by the correspondingly recorded radar data 103. Thus, the parallel processing of radar data 103 or additional sensor information 104 from additional sensor elements in the various processing paths 102 enables accelerated wall diagnosis.

[0101] Alternatively, different functions of the wall diagnosis can also be implemented in different processing paths 102. Thus, for example, wall type classification can be implemented in one processing path 102, and the wall type 123 of the wall 105 to be inspected can be determined. In another processing path 102, object recognition of objects 113 arranged in the wall can be implemented. In this case, object detection with determination of the object position 115 and object classification with determination of the object type 113 can be implemented in one processing path 102.

[0102] Alternatively, object detection and object classification can also be performed in two separate processing paths 102. In the other processing path 102, object depth determination (i.e., determination of object depth 119) and / or determination of object extension 121 can be performed separately. In the aggregation module 165, the different partial results of the wall diagnosis can be aggregated into the corresponding diagnostic result 109.

[0103] The diagnostic module 107 can be divided into different artificial intelligences 125, as already described. Figure 2 As presented in the embodiment of FIG. , the diagnosis module 107 may include, for example, a wall type classification module 129 and an object recognition module 131. The object recognition module can be further divided into an object detection module and an object classification module. The diagnosis module 107 may also include an object depth determination module and an object extension scale module, which are configured to determine an object depth 119 and an object extension scale 121, respectively.

[0104] The corresponding modules can each be designed as an independent artificial intelligence 125, such as a neural network. Alternatively, the different modules can form parts of an overall artificial neural network, which are connected to form the overall neural network according to structures known from the prior art.

[0105] Figure 4A schematic illustration of a measurement of a measuring device 100 according to one specific embodiment is shown.

[0106] For preprocessing, radar data 103 or additional sensor information 104 from other sensors can be normalized, in particular, to ensure numerical stability in the subsequent steps performed by diagnostic module 107 during wall diagnosis. For example, amplitude and / or offset compensation can be performed for this purpose. Furthermore, radar data 103 can be filtered to reduce interfering elements, and the corresponding sensor data can be downsampled to reduce the data rate. Furthermore, radar data 103 or additional sensor information 104 can be converted to the desired frequency range or time range. Methods known from the prior art can be used for this purpose.

[0107] Furthermore, recorded radar data 103 or additional sensor information 104 can be divided into time windows or spatial windows 167. Time windows 167 can be generated by recording radar data 103 or additional sensor information or preprocessed radar data 103 at fixed time intervals. Conversely, spatial windows 167 can be generated by assigning radar data 103 or additional sensor information 104 to the position of measuring device 100 relative to wall 105 along direction of movement 153.

[0108] Figure 4 Graph a) shows such a data matrix resulting from the above steps. The data matrix in window 167 shown in graph a) shows a plurality of sensor data, which may include, for example, radar data 103 or additional sensor information 104 from other sensors, plotted along a frequency channel axis 171 and along a space / time axis 169.

[0109] The width of the time window 167 can be selected in such a way that different sampling rates of the sensors can be compensated and a new window 167 can be provided frequently enough so that the diagnosis result 109 of the wall diagnosis can be displayed on the display unit 111 without a significant time delay during the measurement or shortly after the end of the measurement of the measuring device 100.

[0110] For this purpose, a data recording rate of 2 to 20 windows per second of sensor data may be advantageous. For the spatial windows, the spatial sampling rate may be selected such that the desired position accuracy is achieved. A sampling rate of 1 mm to 1 cm may be advantageous. This means that for every 1 mm to 1 cm of movement of measuring device 100 along direction of motion 153 , corresponding sensor data is recorded.

[0111] The width of spatial window 167 can be selected such that a window contains coherent information about an object 113. A width of 1 to 20 centimeters for the respective spatial window 167 can be advantageous. This results in 4 to 100 measured values ​​per window 167. This enables efficient further algorithmic processing of the correspondingly recorded radar data 103 or additional sensor information by diagnostic module 107.

[0112] Here, once one or more sampling points are available, another time window 167 or spatial window 167 may be provided.

[0113] The diagnosis module 107 can be configured such that as input data, for example also as Figure 3 The input data of each processing path 102 in the embodiment of the present invention is recorded in a matrix corresponding to the window size of the corresponding spatial or temporal window 167. Figure 2 In this embodiment, the corresponding input data may include respectively preprocessed sensor data, namely radar data 103 and additional sensor information 104 of additional sensors.

[0114] As described above, wall diagnosis can be performed by diagnostic module 107 based on a correspondingly trained artificial intelligence. Alternatively, different processing paths can also be calculated using rule-based algorithms. It is also possible to combine artificial intelligence and rule-based algorithms within a single processing path 102, either in parallel or in series.

[0115] The diagnostic results 109 of the wall diagnosis can be clearly expressed as numerical values, vectors, or matrices. Furthermore, for object detection, the probability of detection can be given, or for wall type classification or object classification, the probability of the specified object class or wall type class can be given. The same applies to position and / or depth determination, for which corresponding probability values ​​can also be given.

[0116] If, in addition to radar data 103 , additional sensor information from other sensor types is processed in a processing path 102 , this sensor information can either be merged within artificial intelligence 125 or combined by rule-based combination.

[0117] exist Figure 3 In the embodiment of the present invention, in the post-processing of each processing path 102, multiple algorithm results based on multiple windows 167 can be aggregated by an aggregation module 165. This aggregation can be achieved in particular by majority voting, summing or multiplication of consecutive probability values.

[0118] Furthermore, by clustering a plurality of results, for example, clustering a plurality of objects detected close to one another, it is possible to identify which objects are the same object, so that these objects are not mistakenly identified multiple times.

[0119] It is also possible to apply a weighting function 177 in a multiplicative manner when aggregating the results from a plurality of windows 167. Advantageously, the diagnostic partial results 175 corresponding to the respective data points in space can be weighted with reference to the positioning of the diagnostic partial results 175 relative to the center point of the respective window 167. This is illustrated by way of example in diagram b), in which the individual diagnostic partial results 175 are weighted with reference to the center point of the illustrated window 167 according to the illustrated weighting function 177.

[0120] According to one embodiment, the result of one processing path 102 can influence the extension of another processing path 102 after post-processing 163. In this case, weighting parameters can be adjusted, which can be related to the corresponding result from the processing path 102 for each window.

[0121] For example, the result of the object classification, in which object types 117 of objects arranged in wall 105 are defined, can be used to increase the weight of the wall type classification in which wall type 123 of the corresponding wall 105 is determined at locations where there are no objects 113 in post-processing, because the corresponding radar data 103 at these locations are less affected by reflections from objects 113.

[0122] Figure 5 A further schematic illustration of a measuring device 100 according to another specific embodiment is shown.

[0123] Figure 5 Graphs a) and b) of FIG. 1 show two different alternatives for the joint data processing of radar data 103 and additional sensor information 104 by diagnostic module 107 .

[0124] Diagram b) shows the joint processing of radar data 103 and additional sensor information 104 from an additional sensor by diagnostic module 107. To this end, radar data 103 and additional sensor information 104 serve as input data for diagnostic module 107, which is designed as an artificial intelligence (particularly, as an artificial neural network). Diagnostic module 107 includes a plurality of convolutional layers 108 and a plurality of dense layers 106. Radar data 103 and additional sensor information 104 are processed together as input data via convolutional layers 108 and dense layers 106. The diagnostic result 109, already mentioned, is generated as output data from diagnostic module 107 based on this data.

[0125] In contrast, in diagram b), radar data 103 and additional sensor information 104 serve as independent input data for diagnostic module 107. Diagnostic module 107 is divided into a plurality of processing paths 102. Each processing path 102 includes a plurality of convolutional layers 108 and at least one dense layer 106. In different processing paths 102, diagnostic module 107 generates a wall diagnosis based on radar data 103 or additional sensor information 104 separately from one another.

[0126] In an additional connection layer 148, the partial results of the partial diagnosis of the different processing paths 102 are combined and fed to the last dense layer 106. The output data of the diagnosis module 107 correspond to the diagnosis result 109 described above.

[0127] The correspondingly designed diagnostic module 107 is configured to carry out the above-described wall diagnosis with the above-described features based on the radar data 103 and the additional sensor information 104 .

[0128] In the embodiment shown, the diagnosis module 107 is designed as an artificial neural network, in particular as a convolutional neural network. A corresponding network architecture with convolutional layers 108, dense layers 106 and connection layers 148 is known from the prior art.

[0129] Figure 6 A flow chart of a method 200 for operating a measuring device 100 according to one specific embodiment is shown.

[0130] To operate measuring device 100, radar data 103 from radar sensor unit 101 of measuring device 100 is first received in a first method step 201. Radar data 103 represents wall 105 to be diagnosed. In addition, additional sensor information 104 from additional sensors of measuring device 100 may also be received.

[0131] In a further method step 203 , diagnostic module 107 of measuring device 100 then performs a wall diagnosis based on radar data 103 or additional sensor data 104 by analyzing radar data 103 or additional sensor information 104 and providing a corresponding diagnostic result 109 .

[0132] To this end, in a further method step 205, object recognition is performed by the diagnostic module 107 on an object arranged in the wall 105. Object recognition includes object detection, in which an object position 115 of the object 113 in the wall 105 is determined, and object classification, in which an object type 117 of the object 113 is determined.

[0133] In a further method step 207 , diagnosis result 109 of the wall diagnosis is displayed on display unit 111 of measuring device 100 . Diagnosis result 109 includes at least object position 115 in wall 105 and object type 117 of object 113 .

[0134] Figure 7 A further flow chart of a method 200 for operating a measuring device 100 according to another specific embodiment is shown.

[0135] The embodiment shown is based on Figure 6 and includes all the features described therein.

[0136] In the embodiment shown, method step 203 first includes method step 215. In method step 215, radar data 103 or additional sensor information 104 are pre-processed by first pre-processing module 135 and input data 133 are provided to wall type classification module 129. For this purpose, Figure 2 In the embodiment of FIG, the first pre-processing module 135 can implement S matrix simplification 155.

[0137] In method step 209 , a wall type classification is carried out by the wall type classification module 129 based on the input data 133 and the wall type 123 of the wall 105 to be inspected is determined.

[0138] Furthermore, the wall type classification module 129 provides wall type information 139 , which includes the previously determined wall type 123 of the wall 105 .

[0139] In a further method step 217 , the radar data 103 or the additional sensor information 104 are pre-processed by the second pre-processing module 137 taking into account the wall type information 139 provided by the wall type classification module 129 . Figure 2 In the embodiment of the present invention, the second pre-processing module can perform background correction 157 on the radar data 103 or the additional sensor information 104 while taking into account the wall type information 139. In the background correction, the information of the wall type 123 of the wall is used to correct the radar data 103 or the additional sensor information 104.

[0140] Depending on the respective existing wall type 123 of the wall to be inspected, deviations may occur in the received radar data 103 or sensor information 104. These deviations can be corrected taking into account the existing wall type 123, so that the corrected radar data 103 or additional sensor information 104 provide information that is independent of the wall type.

[0141] Furthermore, the wall-type-corrected data can be subjected to an inverse fast Fourier transform 159 by the second pre-processing module 137. Additionally, focusing and shifting 161 can be performed. Based on this, the input data 133 are provided to the object recognition module 131 by the second pre-processing module 137.

[0142] In method step 205 , object recognition module 131 performs object recognition of object 113 arranged in wall 105 on input data 133 , including object detection and object classification.

[0143] Furthermore, in method step 211, an object depth determination can be performed for object depth 119 of object 113 in wall 105. This can in turn be determined by a correspondingly configured object recognition module 131. In this case, the object depth describes the distance of object 113 from the surface of wall 105.

[0144] Furthermore, in method step 213 , the object extension 121 of the object 113 along the predefined direction may be determined by the object recognition module 131 .

[0145] Figure 8 A further flow chart of a method 200 for operating a measuring device 100 according to another specific embodiment is shown.

[0146] The embodiment shown is based on Figure 6 、 Figure 7 and includes all the features described therein.

[0147] In the embodiment shown, movement data from movement detection unit 141 of measuring device 100 are first received in method step 219 . The movement data maps the movement of measuring device 100 relative to wall 105 between a plurality of positions of measuring device 100 relative to wall 105 .

[0148] In a further method step 221 , it is checked based on the movement data of the movement detection unit 141 whether a movement of the measuring device 100 relative to the wall 105 by a predefined distance is detected.

[0149] If no such movement of measuring device 100 relative to wall 105 is detected, radar data 103 or additional sensor information 104 from the time period between the previous check and the most recently performed check are stored in method step 223. Furthermore, a new check for movement is carried out.

[0150] If such a measuring device 100 is detected to have moved a predefined distance relative to a wall 105, a wall diagnosis is performed by a diagnostic module 107 based on the radar data 103 or the additional sensor information 104 according to the above-described embodiments. The wall diagnosis is performed while the measuring device 100 is moving relative to the wall. The wall diagnosis is divided into a number of sequentially performed segments, and partial results of the wall diagnosis are determined in the different segments. The partial results describe the partial objects identified by the diagnostic module 107 during object recognition.

[0151] The partial object may be, for example, a section of a continuous object 113, since it is scanned by measuring device 100 during its movement relative to wall 105. The section of continuous object 113 represented by the partial object results from a predefined distance covered by measuring device 100 during its movement relative to wall 105.

[0152] In a further method step 225, the partial objects identified during the wall diagnosis are combined into a coherent object 113. The partial objects are combined or combined into a coherent object 113, in particular if there are predefined distance dependencies between the partial objects. The distance dependencies describe whether the partial objects are parts of a coherent object 113.

[0153] The aggregation of partial results may also involve Figure 3 and Figure 5 The radar data 103 and the additional sensor information 104 are processed separately in processing paths 102 that are implemented separately from each other. The partial results generated in processing paths 102 are then combined to form an overall result.

[0154] Furthermore, some of the results may relate to different aspects of the wall diagnosis. Thus, object position 115, object type 117, object depth 119 and / or object extent 121, as in Figure 3 As described in the embodiment of FIG, the results can be determined as separate partial results in different processing paths 102. In the summary, the different partial results can be combined into an overall result, namely a coherent object 113 of the determined object type 117 and the determined object extent 121, which is arranged in the wall 105 at the object position 115 at the determined object depth 119.

[0155] As described above, wall diagnostics can be performed by measuring device 100 based on windows 167 of radar data 103 or additional sensor information 104. Window 167 describes the spatial region of wall 105 to be inspected that was recorded by measuring device 100 recording radar data 103 or additional sensor information 104 at a specific position relative to wall 105. As measuring device 100 moves relative to the wall, multiple windows 167 are recorded temporally sequentially, and these (possibly overlapping) windows coherently map the spatial region of wall 105 scanned by the movement of measuring device 100 relative to the wall.

[0156] Thus, by combining different windows 167, a coherent spatial region can be examined by wall diagnosis, and objects 113 located in this spatial region can be detected and classified as coherent objects. Combining multiple windows 167 can be achieved, for example, by clustering at multiple measuring points. A measuring point describes the position of measuring device 100 relative to the wall at which radar data 103 or additional sensor information 104 was recorded.

[0157] If the measurement points recorded in the corresponding windows 167 are considered coherent due to the predetermined geometric spacing between the measurement points, the windows 167 are then merged using clustering. This merging or merging can also be understood as grouping. If such an object 113 is present, an object 113 can be detected in each window 167. Alternatively, no such object 113 is detected in a window 167. When an object 113 is detected, the object position 115, the corresponding object type 117, the object depth 119, and / or the object extent 121 are then determined.

[0158] By moving the measuring device 100 relative to the wall 105 past an extended object 113 arranged in the wall 105 and recording corresponding data, the object 113 can be scanned from different viewing angles. This increases the data diversity and enables more robust object recognition.

[0159] According to one embodiment, the diagnosis module 107 is configured as a correspondingly trained artificial intelligence that is configured to perform the above-mentioned wall diagnosis. The training of the artificial intelligence can be performed using training methods known from the prior art.

[0160] To this end, the training data set used for training can include radar data 103 and, if necessary, additional sensor information 104, which respectively map a wall 105 of different wall types 123 with different objects 113 arranged in the wall 105, which are respectively arranged at different object positions 115 at different object depths 119 in the wall 105 and include different object extensions 121.

[0161] In this case, the corresponding data of the training data set are labeled using a feature labeling method, whereby the corresponding data are classified with respect to the corresponding wall type 123, the presence of an object 113, the object position 115, the object type 117, the object depth 119, and / or the object extension 121. The corresponding radar data 103 or the additional sensor information 104 can be based on actual measurements of an actual wall 105 by the corresponding measuring device 100, for example.

[0162] For this purpose, a corresponding measuring device 100 can be used to generate a training data set in order to record a corresponding data set from a real wall 105 with an object 113 actually arranged therein. Alternatively or additionally, the data of the training data set can include laboratory data that, for example, simulate only corresponding radar data 103 from a wall 105 with an object 113 or additional sensor information 104.

[0163] Alternatively or additionally, the data of the training data set can be based on measurements of the measuring device 100, which are carried out on a pre-prepared laboratory wall, wherein the object position 115, the object type 117, the object depth 119 and / or the object extension 121 of an object 113 arranged in the wall 105, as well as the wall type 123 of the wall 105 are known.

[0164] Here, the training of artificial intelligence 125 can be aimed at performing object recognition including object detection and object classification of object 113. Artificial intelligence 125 can also be trained to determine wall type 123. Artificial intelligence 125 can also be trained to determine object depth 119. Artificial intelligence can also be trained to determine object extent 121.

[0165] Alternatively, the diagnostic module 107 may include a plurality of artificial intelligences 125 that are each trained to perform object recognition, wall type classification, object depth determination, or object extent determination.

[0166] Figure 9A schematic illustration of a computer program product 500 is shown, which includes instructions which, when the program is executed by a data processing unit, cause the data processing unit to carry out the method 200 for operating the measuring device 100 .

[0167] In the embodiment shown, the computer program product 500 is stored on a storage medium 501. In this case, the storage medium 501 may be any storage medium known from the prior art.

Claims

1. A computer-implemented method (200) for operating a measuring device (100), in particular a wall diagnostic device, comprising: Receiving (201) radar data (103) from a radar sensor unit (101) of the measuring device (100), wherein the radar data (103) maps a wall (105) to be diagnosed; The diagnostic module (107) of the measuring device (100) performs (203) wall diagnosis by analyzing the radar data (103) and provides a diagnostic result (109), wherein the wall diagnosis includes: Performing (205) object recognition on an object (113) arranged in the wall (105) by the diagnostic module (107), wherein the object recognition includes object detection and object classification; and A diagnosis result (109) of the diagnosis module (107) is provided (207) to a display unit (111) of the measuring device (100) so as to display the diagnosis result (109) to a user of the measuring device (100), wherein the diagnosis result (109) includes at least an object position (115) of the object (113) in the wall (105) and an object type (117).

2. The method (200) according to claim 1, wherein: The wall diagnosis further includes: The diagnosis module (107) performs (209) a wall type classification and determines the wall type (123) of the wall (105).

3. The method (200) according to claim 1 or 2, wherein: The wall diagnosis further includes: An object depth determination is performed (211) by the diagnostic module (107) and an object depth (119) of the object (113) in the wall (105) is determined, wherein the object depth (119) is defined as the distance from the object (113) to the surface of the wall (105).

4. The method (200) according to any one of the preceding claims, wherein: The analysis of the radar data (103) further includes: An object extension determination is performed (213) by the diagnosis module (107) and an object extension (121) of the object (113) along a predefined direction is determined.

5. The method (200) according to any one of the preceding claims, wherein: The wall diagnosis further includes: Preprocessing (215) the radar data (103) of the radar sensor unit (101) by a first preprocessing module (135) and providing input data (133) to a wall type classification module (129) of the diagnosis module (107); The wall type classification module (129) of the diagnosis module (107) performs (209) wall type classification based on the input data (133) provided by the first pre-processing module (135) and provides wall type information (139); Preprocessing (217) the radar data (103) by a second preprocessing module (137) of the diagnosis module (107) taking into account the wall type information (139) and providing input data (133) to an object recognition module (131) of the diagnosis module (107); The object recognition module (131) performs (205) object recognition based on the input data (133) provided by the second pre-processing module (137) and provides a diagnosis result.

6. The method (200) according to any one of the preceding claims, wherein: Receiving radar data (103) from a plurality of positions of the measurement device (100) relative to the wall (105), wherein the method (200) further comprises: Receiving (219) motion data from a motion detection unit (141) of the measuring device (100), wherein the motion data maps the motion of the measuring device (100) relative to the wall (105) between a plurality of positions of the measuring device (100) relative to the wall (105).

7. The method (200) according to any one of the preceding claims, further comprising: Checking (221) based on the motion data of the motion detection unit (141) whether it is detected that the measuring device (100) has moved a predefined distance relative to the wall (105); If no motion is detected, storing (223) the radar data (103) and re-performing the check; If movement of the predefined distance is detected, the wall diagnosis is performed (203) based on the radar data (103) by the diagnosis module (107).

8. The method (200) according to any one of the preceding claims, wherein: The wall diagnosis further includes: If a predefined distance correlation exists between partial objects detected and classified based on radar data (103) received at different positions of the measuring device (100) relative to the wall (105), the partial objects are combined (225) into a coherent object (113).

9. The method (200) according to any one of the preceding claims, wherein: The measuring device (100) further comprises at least one inductive sensor and / or eddy current sensor and / or capacitive sensor and / or AC sensor and / or nuclear magnetic resonance sensor and / or ultrasonic sensor for providing additional sensor data, wherein the diagnostic module (107) is configured to perform the wall diagnosis taking into account the additional sensor data.

10. The method (200) according to any one of the preceding claims, wherein: The diagnostic module (107) comprises at least one correspondingly trained artificial intelligence (125) configured to perform object recognition and / or wall classification and / or object depth determination based on the radar data (103) and / or the additional sensor data.

11. A computing unit (151) configured to carry out the method (200) for operating a measuring device (100) according to any one of the preceding claims 1 to 10.

12. A computer program product (500), comprising instructions which, when executed by a data processing unit, cause the data processing unit to carry out the method (200) for operating a measuring device (100) according to any one of claims 1 to 10.

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